Abstract
Presentation attacks on face recognition systems are classified into two categories: physical and digital. While much research has focused on physical attacks such as photo, replay, and mask attacks, digital attacks such as morphing have received limited attention. With the advancements in deep learning and computer vision algorithms, several easy-to-use applications are available where with few taps/clicks, an image can be easily and seamlessly altered. Moreover, generation of synthetic images or modifying images/videos (e.g. creating deepfakes) is relatively easy and highly effective due to the tremendous improvement in generative machine learning models. Many of these techniques can be used to attack the face recognition systems. To address this potential security risk, in this research, we present a novel algorithm for digital presentation attack detection, termed as MagNet, using a “Weighted Local Magnitude Pattern” (WLMP) feature descriptor. We also present a database, termed as ID Age nder, which consists of three different subsets of swapping/morphing and neural face transformation. In contrast to existing research, which utilizes sophisticated machine learning networks for attack generation, the databases in this research are prepared using social media platforms that are readily available to everyone with and without any malicious intent. Experiments on the proposed database, FaceForensic database, GAN generated images, and real-world images/videos show the stimulating performance of the proposed algorithm. Through the extensive experiments, it is observed that the proposed algorithm not only yields lower error rates, but also provides computational efficiency.
Highlights
The high performance of modern face recognition algorithms and the convenience of capturing face images have supported the applications to allow remote or unsupervised face images for authentication (Majumdar et al, 2017)
The performance of the proposed algorithm is compared with local binary pattern (LBP), binarized statistical image features (BSIF), and local phase quantization (LPQ), which were the top three features on the Snapchat database and most popular in the literature of digital attack detection
The proposed features show an improvement of 78% in terms of Average Classification Error Rate (ACER) from the second best-performing feature, i.e., LBP;
Summary
The high performance of modern face recognition algorithms and the convenience of capturing face images have supported the applications to allow remote or unsupervised face images for authentication (Majumdar et al, 2017). Online banking can be performed via face authentication While this increases convenience and reduces fraudulent access, the security of these recognition systems is an important task. Presentation Attacks are defined as “the attack on the system which in any way can affect the decision of a biometric system”. They can be broadly classified into two categories: digital and physical. Digital presentation attacks include attacks such as morphing, swapping, and digital alterations. These attacks can be performed for multiple reasons, avoiding recognition, impersonating someone else’s identity, or multiple people sharing an identity. J. et al, 2014; Goswami et al, 2018, 2019; Akhtar and Mian, 2018)
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